Planning and Control of Robot Motion based on Time-Scale Transformation and Iterative Learning Control

To achieve the control of a small-sized robot manipulator, we focus on an actuator using a shape memory alloy (SMA). By providing an adjusted voltage, an SMA wire can itself generate heat, contract, and control its length. However, a strong hysteresis is generally known to be present in a given heat and deformation volume. Most of the control methods developed thus far have applied detailed modeling and model-based control. However, there are many cases in which it is difficult to determine the parameter settings required for modeling. By contrast, iterative learning control is a method that does not require detailed information on the dynamics and realizes the desired motion through iterative trials. Despite pioneering studies on the iterative learning control of SMA, convergence has yet to be proven in detail. This paper therefore describes a stability analysis of an iterative learning control to mathematically prove convergence at the desired length. This paper also details an experimental verification of the effect of convergence depending on the variation in gain.

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Time-Scale Transformed Iterative Learning Control for a Class of Nonlinear Systems With Uncertain Trial Duration

IEEE Transactions on Control Systems Technology ◽

10.1109/tcst.2019.2920615 ◽

2020 ◽

Vol 28 (5) ◽

pp. 1972-1979 ◽

Cited By ~ 3

Author(s):

Patrick M. Sammons ◽

David Hoelzle ◽

Kira Barton

Keyword(s):

Nonlinear Systems ◽

Time Scale ◽

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Trial Duration

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Iterative Learning Identification/Iterative Learning Control for Linear Time-Varying Systems

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4033630 ◽

2016 ◽

Vol 138 (10) ◽

Cited By ~ 4

Author(s):

Nanjun Liu ◽

Andrew Alleyne

Keyword(s):

Iterative Learning Control ◽

Linear Time ◽

Tracking Error ◽

Learning Control ◽

Iterative Learning ◽

Parameter Estimates ◽

Time Varying ◽

Fixed Parameter ◽

Time Varying Systems ◽

And Control

This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.

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An Iterative Learning Control Design Method for Nonlinear Discrete-Time Systems with Unknown Iteration-Varying Parameters and Control Direction

Mathematical Problems in Engineering ◽

10.1155/2016/8971407 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Duo Zhao ◽

Yong Yang

Keyword(s):

Discrete Time ◽

Iterative Learning Control ◽

Design Method ◽

Learning Control ◽

Convergence Condition ◽

Iterative Learning ◽

Unknown Parameters ◽

Varying Parameters ◽

Control Direction ◽

And Control

An iterative learning control (ILC) scheme is designed for a class of nonlinear discrete-time dynamical systems with unknown iteration-varying parameters and control direction. The iteration-varying parameters are described by a high-order internal model (HOIM) such that the unknown parameters in the current iteration are a linear combination of the counterparts in the previous certain iterations. Under the framework of ILC, the learning convergence condition is derived through rigorous analysis. It is shown that the adaptive ILC law can achieve perfect tracking of system state in presence of iteration-varying parameters and unknown control direction. The effectiveness of the proposed control scheme is verified by simulations.

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Iterative learning control of an inverse novel ball screw transmission system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189627 ◽

2021 ◽

pp. 1-10

Author(s):

Wen-Hsiang Hsieh ◽

Yi-Syun Chen ◽

Shang-Teh Wu

Keyword(s):

Iterative Learning Control ◽

Controller Design ◽

Learning Control ◽

Transmission System ◽

Iterative Learning ◽

Ball Screw ◽

Lagrange Equations ◽

Control Approach ◽

Dynamic Analyses ◽

And Control

Iterative Learning Control is a branch of intelligent control which combines artificial intelligence and control theory. This objective of this study aims at reducing the cyclic error of an inverse ball screw transmission system by using iterative learning control approach. Firstly, kinematic and dynamic analyses are conducted by using the vectorial loop closure and Lagrange equations, respectively. Then, system identification is performed followed by controller design. Moreover, controller parameters are optimized to minimize the error. Finally, the feasibility and the effectiveness of the proposed approach are verified by computer simulation and prototype experiment. The experimental results showed that the reducing percentage of the square error sum of the output speed is 90.64% by using PID control only. If ILC is applied additionally, the error is further reduced to 94.21%. Therefore, the proposed approach is not only feasible and but also effective.

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